Platform for temporary and intermittent transfers

ABSTRACT

Provided are systems and methods for auto-performing short-term investments on an intermittent basis via a crypto-network and returning the principal and any interest before the account holder needs the money back. For example, a method may include executing a machine learning model on ingested data records to identify a temporary value that is idle in an account and a period of time that the temporary value is idle, displaying the determined temporary value, the period of time, and one or more recommendations of one or more blockchain networks, receiving authorization and a selection of a blockchain network from a user interface, installing a pre-programmed blockchain wallet on a blockchain ledger of the selected blockchain network, and triggering a transfer of funds from the account of the user to the pre-programmed blockchain wallet via a crypto-exchange server which converts the funds to cryptocurrency prior to the transfer.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit under 35 USC § 119(e) of U.S.Provisional Patent Application No. 63/302,594, filed on Jan. 25, 2022,in the United States Patent and Trademark Office, the entire disclosureof which is incorporated herein by reference for all purposes.

BACKGROUND

Traditional bank accounts do not provide much interest for an accountholder. As an example, a savings account or a checking account may earnan account holder (e.g., a person, a group of people, an organization,etc.) between 0.1% interest and 0.5% interest on the funds in theaccount over the course of a year. The interest is usually not very muchespecially for smaller invested amounts. Furthermore, understanding howto invest your money in a way that is safe and profitable is a dauntingtask. Investments typically require long-term commitments in order forthe investment to mature into any significant value. During this time,which can be over many years, the invested money is untouchable to theaccount holder. Therefore, keeping money in your savingsaccount/checking account may be a safer way to grow wealth because itkeeps your money available in case of unexpected expenses.

SUMMARY

The example embodiments are directed to a host platform, such as amobile application host, which may be accessed by a user via a mobiledevice (e.g., a smart phone) by the user installing a front-endapplication of the mobile application on their device. The applicationmay be downloaded and installed from an application marketplace or thelike. The user (account holder) may then enter information aboutthemselves as well as a savings account number or checking accountnumber. In response, the host platform may periodically pull accountbalance information (e.g., total balance, debits, credits, etc.) fromthe user's account from a period of time (e.g., the last 5 years, etc.)and analyze the information using machine learning.

For example, the account balance and history information may be inputinto a machine learning service which includes one or more machinelearning models which can predict temporary investments using funds forthe account holder's account that can return more interest than what theaccount holder is currently making from their bank. In particular, themodels can predict an amount of “idle cash” or idle value which iscurrently held in the bank account and which can safely be investedwithout risk of the account going into default on a future payment. Themodels can also predict a period of time for the investment (e.g., 10days, 15 days, 20 days, etc.) which also relieves the user from risk ofmissing any payments, bills, etc. The machine learning service canidentify these attributes based on patterns of the account balance overtime.

Furthermore, the host platform may transfer funds from the user's bankaccount to an investor system, such as a crypto-exchange server, whichinvests the funds in crypto-based assets such as staking the investmenton a stable coin blockchain network, or some other crypto-investmentvehicle. The transferred funds (e.g., fiat currency) may be convertedinto cryptocurrency by the partner system and staked or otherwisetransferred to a blockchain network where the investment is beingperformed.

Here, the transfer is temporary. For example, the investment may remainwithin the blockchain network until a predefined interest some defaultcondition such as a value threshold being obtained (e.g., the money hasearned 8%, etc.) or until a predetermined period of time has elapses(e.g., 10 days, 15 days, 20 days, etc.) In some embodiments, the hostplatform may set a default condition for the return of the investmentwhile also monitoring for stop conditions which, if detected, cause thehost platform to terminate the investment earlier than expected (i.e.,prior to the default time of return). When the return is desired, thehost platform may trigger the investment system to transfercryptocurrency from the investment including the interested earned backto a wallet controlled by the investment system (and accessible to thehost and the user), and converted back into fiat currency. Furthermore,the investment system may transfer to the fait currency back to the uservia traditional payment means (wire transfer, check, cash, etc.) Asanother example, the host platform may transfer the funds back via ablockchain network.

Other features and aspects may be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the example embodiments, and the manner inwhich the same are accomplished, will become more readily apparent withreference to the following detailed description taken in conjunctionwith the accompanying drawings.

FIGS. 1A-1D are diagrams illustrating a temporary investment processusing machine learning in accordance with example embodiments.

FIGS. 2A-2D are diagrams illustrating a process of monitoring atemporary investment for stop conditions in accordance with exampleembodiments.

FIG. 3A is a diagram illustrating a communication sequence of atemporary investment process in accordance with example embodiments.

FIG. 3B is a diagram illustrating a communication sequence of a securitymonitoring process in accordance with example embodiments.

FIG. 4 is a diagram illustrating a process of providing a user withoptions for selecting a temporary investment in accordance with exampleembodiments.

FIG. 5 is a diagram illustrating a method for recommending andperforming a temporary investment in accordance with exampleembodiments.

FIG. 6 is a diagram illustrating a method for monitoring a temporaryinvestment in accordance with example embodiments.

FIG. 7 is a diagram illustrating a computing system for use with any ofthe example embodiments described herein.

Throughout the drawings and the detailed description, unless otherwisedescribed, the same drawing reference numerals will be understood torefer to the same elements, features, and structures. The relative sizeand depiction of these elements may be exaggerated or adjusted forclarity, illustration, and/or convenience.

DETAILED DESCRIPTION

In the following description, specific details are set forth in order toprovide a thorough understanding of the various example embodiments. Itshould be appreciated that various modifications to the embodiments willbe readily apparent to those skilled in the art, and the genericprinciples defined herein may be applied to other embodiments andapplications without departing from the spirit and scope of thedisclosure. Moreover, in the following description, numerous details areset forth for the purpose of explanation. However, one of ordinary skillin the art should understand that embodiments may be practiced withoutthe use of these specific details. In other instances, well-knownstructures and processes are not shown or described in order not toobscure the description with unnecessary detail. Thus, the presentdisclosure is not intended to be limited to the embodiments shown, butis to be accorded the widest scope consistent with the principles andfeatures disclosed herein.

The example embodiments are directed to a host platform, such as a cloudplatform, web server, distributed database, etc., which hosts a softwareapplication such as a mobile application, a web application (e.g., aprogressive web application, etc.), or the like, which can be downloadedfrom an application marketplace and installed on a user device such as amobile phone, a tablet, a laptop, a smart-wearable, a personal computer,a server, and the like. The application may have a front-end client thatruns on the user's device and a back-end that is hosted on the hostplatform and that communicates with the front-end of the application onthe user's device via a network such as the Internet.

A user may input a bank account number or other account identifyinginformation, and the application may contact the corresponding bank thatissued the bank account based on the bank account number. The user mayalso be an “organization” or “group of users” and not just a singleuser. Here, the account being invested may be an account with funds ofmultiple users or an organization that uses the funds for payment orother means with multiple users. In these examples, theuser/accountholder may authorize the host platform to access the bankaccount and perform a temporary withdrawal and investment. The amountthat is withdrawn and the time window for the investment may be manuallyinput by the user. As another example, the host platform may provide oneor more machine learning models which analyze the user's transactionhistory (e.g., over the past few years, etc.) and recommend how muchmoney the user should invest on a temporary basis, and for how much time(e.g., a few days, a few weeks, etc.).

According to various embodiments, the machine learning model(s) mayreceive account balance history information, transaction history,debits, credits, etc. and predict how much money will be safelyavailable (e.g., with a comfortable cushion) to invest for a few days ora few weeks. The model(s) may also identify a time period for theinvestment. If the model(s) determines that $20,000 will be availablefor a two-week period, and then the user will need the money back topurchase supplies, the model(s) can recommend an investment window of 10days for the purpose of the application is to gain valuable interest onthe funds inside the bank account in the short-term. However, instead ofrecommending $20,000, the model may recommend $15,000 as an investmentin order to leave a safety cushion just in case unexpected expensesoccur. The total amount, the cushion, the time period, etc., may beoutput by the model(s) in response to receiving the account historyinformation as input.

As an example, a user's bank account (e.g., savings account, checkingaccount, debit account, etc.) may earn the user 0.3% interest a yearfrom a financial institution that issued the bank account. Over thecourse of a year, the interest earned on a savings account with $100,000to start, will be approximately $300. In contrast, with the softwareapplication described herein, a user's bank account may earn them 5-10%interest a year, or more. Over the course of a year, the interest earnedon the same savings account with $100,000 to start will be approximately$5000-$10,000, or 15 to 30 times more interest than they would haveearned by simply leaving the money in their account without touching itand letting the bank interest accrue. Furthermore, because of themachine learning model, the user can receive the money back prior to anynecessary expenses which can be detected by the machine learningmodel(s) from patterns of spending in the transaction history.

It should be appreciated that the “user” who's account is being investedon a short-term basis may actually be an organization (i.e., with manyusers). Here, the software application may invest a larger portion ofmoney that is only there for a short amount of time, such as from anorganization's excess funds, payroll account, pension account, etc., andmake the organization some significant interest in the short-term.Furthermore, the funds plus interest can be returned after only a fewdays or a few weeks, if necessary. Furthermore, the investment processmay be repeatedly performed on a continuous/iterative basis. Forexample, every month the same process may be performed by the softwareapplication thus maximizing the interest on the account that would notbe realized otherwise.

The investment strategy may vary widely and may include crypto-basedassets such as stablecoins, liquidity pools, cryptocurrency, digitalassets, more traditional investment opportunities (e.g., stocks, bonds,etc.) and the like. Here, the host platform may use an investmentpartner, such as a crypto-exchange, to convert the fiat-based funds fromthe bank account into crypto-based assets such as cryptocurrency andinvest the crypto asset in a stablecoin or other digital asset. The hostplatform may also create a pre-programmed blockchain wallet which itthen installs on a blockchain ledger of the recommended blockchainnetwork (e.g., stablecoin, etc.) where the host platform suggest theuser put their funds. The blockchain wallet, also referred to as adigital wallet, etc., may be pre-programmed to receive cryptocurrencyfrom a crypto-exchange and transfer the received cryptocurrency (or thelike) to a predefined smart contract which is identified by a smartcontract ID, or the like, within the pre-programmed blockchain wallet.

The investment partner may provide the crypto-asset converted from thefiat funds to the pre-programmed blockchain wallet which then stakes theinvestment to a smart contract of a blockchain network, such as astablecoin network. The benefit of using digital/crypto assets is thatthe exchanging and investing of fiat currency into crypto-assets, andvice versa, can be performed in a few seconds on a 24/7 basis, whereastrading stocks, bonds, and other financial assets can require multiplehours or days to transact between users through third-parties, as wellas minimum fees, which may not be ideal when the money is only beinginvested for a few days or a few weeks.

Some of the benefits of the example embodiments include a novel approachto scoring idle cash using an architecture that enables a host platformto pull data, via an API, from one or more user accounts and then usethat data to generate recommendations to the user on how best to addintermittent and temporary investment value.

Another benefit of the example embodiments is risk analysis andmitigation. Here, the host platform may monitor a temporary investmentfor problems and then act if a risk is perceived. For example, the hostplatform may install a smart contract on a blockchain ledger of thestablecoin network and monitor (read) changes and updates to theblockchain ledger. Here, the smart contract may read the ledger andprovide such information to the security monitoring engine which cananalyze the code updates to determine whether a code update may create aproblem (hack, unstable, etc.). For example, the host platform maydetect suspicious code, a suspicious user account, a suspiciousblockchain transaction, or the like, being added to the blockchainledger and pull the temporary investment out of the blockchain ledger inresponse. As another example, the host platform may detect anomalouswallet behavior—for example, someone is taking money in and out rapidly.There is a change to a baseline of a wallet behavior. The host may alsodetect this and pull the money out of the blockchain ledger in response.

Other benefits of the example embodiments include yield optimizationwhich can identify the best yield, yield as a service API, and anend-to-end architecture where a customer remains in control of the fundseven while they are temporarily and intermittently invested by the hostplatform in an automated manner.

The mobile application provided herein may be a platform where a usercan log in and measure their treasury investments and also executeinvestments. For example, if the user wants to buy $50k of Bitcoin, thehost platform may purchase it for the user. If the user hastreasury/assets of any kind the host platform can facilitate thoseinvestments in an automated manner. The system may be a standard way forbusinesses to invest in any asset they like using the application.

FIGS. 1A-1D illustrate a temporary investment process using machinelearning in accordance with example embodiments. FIG. 1A illustrates anoverall architecture 100 of the example embodiments. It should beappreciated that different systems and devices may be present in thenetwork, and this is only for purposes of example, Referring to FIG. 1A,a host platform 120 may host a software application such as a mobileapplication or web application that performs temporary short-terminvestments in an automated fashion based on funds in a user's account.As further described in the examples herein, the host platform 120 mayhost an idle cash recommendation engine 122 (also referred to herein asa recommendation engine 122) which is a machine learning service withone or more machine learning models embodied therein that can be used toidentify a temporary investment amount available within a financialaccount of a user based on historical patterns of account behavioridentified from changes in account balance over time, patterns inspending, and the like. The host platform may also host a securitymonitoring engine shown in FIGS. 2A-2C that can monitor the temporaryinvestment and pull the funds back to the user's account in response toa terminating event such as a security issue with the blockchain ledger.

In FIG. 1A, the user is represented by user device 110. The user maydownload the application (e.g., the mobile app) from an applicationmarketplace or register with the application (e.g., a progressive webapplication, etc.) via a website. The user may provide information aboutthemselves as well as a bank account identifier (e.g., account number,bank ID, etc.) of an account that the user would like to temporarilyinvest from.

The host platform 120 may receive the account identifier, identify acorresponding financial institution 130 that issued the account, andaccess transaction history, account balance history, etc., of the bankaccount from the financial institution, for example, via one or more APIcalls to an API of the financial institution 130. Here, the API call mayinclude an identifier of the bank account of the user. In response, thefinancial institution 130 may provide transaction history from the bankaccount over a predetermined historical period of time (e.g., 2 years, 5years, etc.) and transmit the account history information to the hostplatform 120. The account history may be analyzed by the recommendationengine to identify an amount of value/cash that can be safely invested.

Referring now to FIG. 1B, a process 150 of analyzing the account historyinformation to provide the user with a recommended investment amount andtime window is provided. Here, the user may actually be an organizationwith a payroll account where money typically sits and earns 0.2%interest. In this example, the host platform 120 may operate/access oneor more machine learning models included within the recommendationengine 122 that can analyze the transaction history of the organizationand provide a “safe” investment amount as well as a period of time inwhich the amount can be invested. According to various embodiments, therecommendation engine 122 may use the machine learning models (e.g.,machine learning models 124 and 126, etc.) to identify a “floor” ofvalue within the user's account which the user does not want to gobelow, and also add a cushion (e.g., a few thousand dollars, etc.) tothe floor for additional safety and an amount of time that the value canbe temporarily invested and then returned. Everything above the floorand the cushion may be recommend for investment. However, there may bepredefined limits such as default limits or user-configured limits onthe amount to invest. The safe investment amount may be determined suchthat a safe remaining balance remains in the bank account to coverexpected expenses as well as some unexpected expenses rather than beinginvested.

For example, the organization may have $1,000,000 dollars in theirpayroll account. The one or more machine learning models may analyze thetransaction history of the user including account balance history,expenses, timing information, etc., and identify a pattern of spendingbehavior and also a pattern of the account balance. Here, the machinelearning models 124 and/or 126 may learn that the payroll account alsois used for making payments on supplies every 3 months that can be of asignificant expense (e.g., $75,000). The machine learning models 124and/or 126 may also learn that the payment for supplies is to occur in 1week. Therefore, the one or more machine learning models 124 and/or 126may determine that $925,000 of the account balance is safe to invest.For example, the machine learning model 124 may be designed to determinea safe investment amount while the machine learning model 126 determinesa temporary investment time period. It should also be appreciated thatone machine learning model may determine both the amount and the time.In some embodiments, to incorporate a cushion, the recommendation engine122 may add in a buffer of value to prevent the account from beingoverdrawn, for example, a buffer/cushion of $25,000. Therefore, therecommendation engine 122 may recommend a total investment amount of$900,000.

Furthermore, the machine learning model 126 may learn that the payrollaccount has a significant amount of historical fluctuation during thefollowing month, for example, because of bonuses being paid out toemployees, etc. In this case, the machine learning model 126 mayrecommend that the time window expire before the following month, whichhappens to be 3 weeks away. In this example, the recommendation engine122 may combine the recommendations (temporary value+temporary timelimit) and output a recommendation of investing $900,000 for a total of18 days. This time period is determined by the recommendation engine 122based on subtracting a period of time necessary to return the money tothe account (e.g., 2 days, etc.). This information can be sent by thehost platform 120 to a front-end of the application on the user device110.

Referring now to FIG. 1A and FIG. 1C, the host platform 120 may performa process 160 of transferring the funds from a financial account of theuser held by the financial institution server 130 to a crypto-investor140 for the purpose of a short-term investment only on a blockchainledger 154 of a blockchain network recommended for temporary investmentby the recommendation engine 122. As part of this process, the hostplatform 120 may generate a pre-programmed blockchain wallet 112 whichit installs on the blockchain ledger 162 and which is pre-programmed tomove funds added thereto into predefined investment locations on theblockchain ledger 154 (i.e., predefined smart contracts, etc.) Forexample, details and attributes such as account numbers, accesscredentials, keys, and the like, may be hardcoded into thepre-programmed blockchain wallet 112 thereby ensuring that theinvestment is delivered directly to the location selected by the user.The digital keys for accessing the blockchain wallet 112 may bedelivered to the user device 110 by the host platform 120 thus providingthe user with the ability to review and monitor the funds on their own.

The crypto-investor 140 may be a crypto-exchange configured to convertfiat-based funds, such as cash, into crypto-based assets such ascryptocurrency, etc. Here, the host platform 120 may act as an agent forthe user and receive an authorization from the user device 110 to investa predetermined amount of money for a predetermined amount of time, suchas the $900,000 for 18 days, in the example given above. The hostplatform 120 may also receive “authorization” from the user device 110to automatically pull the money out of the payroll account, and returnthe money to the payroll account within the 18 day period, or less. Inresponse, the host platform 120 may transfer funds from the financialaccount hosted by the financial institution 130 to the crypto-investor140. The crypto-investor 140 may convert the funds (fiat currency) intoa crypto-asset (e.g., Bitcoin, stablecoin, liquidity funds, etc.) andthen invest the crypto-asset in any number of blockchain-based networksincluding blockchains 151, 152, 153, etc., such as stablecoin networkswhich allow staking to smart contracts.

As shown in FIG. 1C, in response to receiving the fiat currency from thehost platform 120, the crypto-investor 140 may convert the fiat currencyinto cryptocurrency and submit/transfer the cryptocurrency to thepre-programmed blockchain wallet 112 installed on the blockchain ledger154 of the blockchain 151. Furthermore, the crypto-investor 140 may giveboth the user of the user device 110 and the host platform 120 access tothe blockchain wallet 112. For example, both the user device 110 and thehost platform 120 may be given respective keys for accessing theblockchain wallet 112. The keys may provide the user of the user device110 with the ability to read information about the investment from theblockchain ledger 154. The pre-programmed blockchain wallet 112 mayexecute a blockchain transaction and submit the blockchain transactionto blockchain peers of the blockchain 151 for storage on the blockchain151. The investment of the temporary value occurs when the blockchaintransaction from the pre-programmed blockchain wallet 112 is committedto the blockchain 151.

Referring now to FIG. 1D, a process 170 of pulling the funds out of theblockchain 151 and returning them back to the user's bank account in anautomated manner is shown. Here, the crypto-investor 140 may have apredetermined investment strategy (e.g., submitted by the user, etc.)that is specified ahead of time. The crypto-investor 140 may temporarilyinvest the cryptocurrency in the blockchain wallet 112 on the blockchainledger 154 until the predetermined period has expired (e.g., the 18 daysare up), until a predetermined amount of interest has been earned (e.g.,10%, etc.), until a stop condition has been detected, and the like. Anyof these conditions (and others) may trigger the crypto-investor 140 topull the crypto-asset out of the blockchain 151, convert it back intofiat-based funds, and return the funds (plus interest) back to theuser's account at the financial institution via the financialinstitution server 130. Here, the crypto-investor 140 maytransfer/sell/exchange the cryptocurrency from the blockchain wallet 112via another blockchain transaction executed by the blockchain peers ofthe blockchain 151, and convert the resulting cryptocurrency back intofiat currency. Next, the crypto-investor may return the fiat currencyback to the financial institution server 130, which automatically putsthe money (with the interest earned by the crypto-investor 140) backinto the user's account at the financial institution 130.

As a result of the example embodiments, a traditional bank account thatearns very little or no interest, can earn significantly more interestby performing intermittent and continuous short-term investments basedon a combination of account history analysis and temporarycrypto-investments that can yield substantially more interest than atraditional bank account. Furthermore, one or more of the host platform120 and the crypto-investor 140 may take a small percentage for theirrole in the process.

Although not shown in the drawings, the host platform 120 may alsoprovide automated “claimless” insurance for digital currency yieldaggregators. In the cryptocurrency yield investing space, users arelooking to take digital currencies like US dollars, etc., stake them(transfer them to a smart contract, chaincode, etc.) and earn interestby either lending the money out to borrowers, or earning fromparticipating in currency exchange fees by providing liquidity. However,one of the biggest fears of users that prevents active participation isloss of principal funds. Even though many of these projects offerovercollateralized models that reduce the risk of ‘investment loss’,there are still cyber related risks that could create loss of funds dueto exploits, bugs and fraud. This is not dissimilar to regular bankdeposit risk where even the FDIC only insures up to $250,000 with a lotmore liquidity and a lot more control and regulation on banks than whatexists in the crypto space.

The most common approach to interest harvesting is investing in vaultsor smart contracts that split the risk to multiple strategies. Thisallows further de-risking of principle loss and also yield optimizationenabling the best return. These vaults are referred to as “yieldaggregators.” Some of them simply invest in multiple vaults and some ofthem do other things like auto compound earnings back into a principalamount to get more yield. For example, a user could invest $100, earn$5, and then the yield aggregator will put the extract $5 back so theuser's account has $105 instead of $100. And the process is allautomatic. Normally these vaults entice users by saying ‘stake throughme and you'll earn more’—in favor of users doing that, these vaults takea profit share of the enhanced earnings.

In the example embodiments, a new kind of vault (yield aggregator) isprovided that provides a negative yield relative to the potential, butin favor, your principal funds are insured. In other words, the yieldaggregator will take a percentage of any growth/interest earned on theprincipal, but the yield aggregator will also cover any losses to theprincipal funds, should the investment go bad/poorly. So instead ofearning 5%, the user may earn 85% of 5% which is equal to 4.25% of thegains instead of the whole 5%. However, if the principal goes down from$100 to $80, the user would not lose anything and the yield aggregatorwould lose $20.

FIGS. 2A-2C illustrate a process of monitoring a temporary investmentfor stop conditions in accordance with example embodiments. As notedpreviously, a temporary investment may include a default terminationpoint such as a particular point in time/date when the funds should bereturned or a predefined amount of interest being earned which triggersthe money to be returned. It should also be appreciated, that unexpectedevents may also be used to terminate a temporary investment before ithas reached its default termination point. For example, the temporaryinvestment may be pulled back early if the host platform detects apossible security issue or other issue such as the user's fiat-basedaccount having a balance fall below a predefined safety threshold, etc.

Referring to FIG. 2A, there is shown a process 200 of a host platform220 configuring a monitoring engine 222 to monitor various external datasources including a financial institution server 210, an external webservice 250 (e.g., a Shopify account, etc.), a blockchain ledger 230,and a blockchain ledger 240. In this example, the host platform 220 hasinvested funds from a financial account of a user held by the financialinstitution server 210 in a blockchain stored on the blockchain ledger230. Also, the host platform 220 has invested funds from the financialaccount of the user held by the financial institution server 210 in ablockchain stored on the blockchain ledger 240.

Here, the monitoring engine 222 may be configured to monitor updates tocontent stored on the blockchain ledgers 230 and 240, respectively, anddetermine whether issues arise that require either investment to bereturned. For example, the host platform 220 may configure themonitoring engine 222 to identify suspicious code updates to blockchainsoftware which are stored on a blockchain ledger, suspicious useraccounts/blockchain wallets that are added to a blockchain ledger,suspicious blockchain transactions stored on a blockchain ledger, andthe like. In this case, the monitoring engine 222 may be configured toanalyze the different blockchain ledgers for stop conditions based oncontent updated on the blockchain ledger. The frequency of such analysismay be performed on a periodic basis (e.g., each time a new block isadded to the blockchain ledger, etc.) As another example, the analysismay be performed on-demand from the user or some other entity.

According to various embodiments, the monitoring engine 222 may maintaina conditions database 224 with rules or statements therein whichidentify stop conditions for terminating a temporary investment. Forexample, the monitoring engine 222 may be configured to identifyparticular code updates that have been previously labeled as a securitythreat. As another example, the monitoring engine 222 may be configuredto identify suspicious blockchain wallet identifiers or useridentifiers. As another example, the monitoring engine 222 may beconfigured to identify suspicious transaction content such as suspiciousamounts, suspicious frequencies of transactions, and the like. Any ofthese suspicious activities may be predefined as stop conditions. Thestop conditions may also have threshold values or limits that must bereached or fallen below to be triggered.

As another example, the monitoring engine 222 may be configured tomonitor the user's account at the financial institution via thefinancial institution server 210. For example, a stop condition mayoccur if an account balance of the user's account at the financialinstitution falls below a predefined threshold balance which may beconfigured by the user via the mobile application, etc. These conditionsmay also be stored in the conditions database 224. As another example,the monitoring engine 222 may monitor the external service 250 which maybe used for purchasing goods from the user (e.g., the user's business,etc.)

In addition to stop conditions, the conditions database 224 may alsostore “start” conditions as well. As an example, the monitoring engine222 may be configured to monitor the user's business transactions withinthe external service 250 (such as a SHOPIFY® account, etc.) to determinewhether a start condition has been met. A start condition may occur whenthe user's business sells a predetermined amount of value (e.g., $1000)or a predetermined number of sales (e.g., 20 sales, etc.) If a startcondition is detected, the monitoring engine 222 may notify the hostplatform 220 which may trigger an auto-investment of temporary valuebased on the sales (e.g., 20% of the sales, etc.) into a new temporaryinvestment for the user.

The monitoring process performed by the monitoring engine 222 may becyclical/repetitive. The frequency at which the monitoring process isrepeated may be every time a new block is added to the respectiveblockchain. As another example, the frequency of the monitoring processmay be predefined based on time (e.g., daily, weekly, monthly, etc.). Asanother example, the frequency of the monitoring process may be dynamic,random, requested by the user, etc. It should also be appreciated thatdifferent external data sources may have different monitoringfrequencies. For example, the blockchain ledger 230 may be monitoredeach time a new block is added (which may be approximately every 5minutes) while the external service 250 may be analyzed once a day, etc.

FIG. 2B illustrates an example of a process 260 of monitoring theblockchain ledger 230 via the monitoring service 222 in accordance withexample embodiments. The monitoring process may be performed when auser's fiat currency is converted and invested into a digital assetmanaged by a blockchain 232 on the blockchain ledger 230. Referring toFIG. 2B, the monitoring engine 222 may install a smart contract 226 onthe blockchain ledger 230 which is configured to monitor updates to theblockchain 232 of the blockchain ledger 230 including transactionscommitted to the blockchain 232, code updates made to the blockchain232, blockchain wallets/users added to the blockchain 232, and the like.

Here, the monitoring service 222 may establish a communication channelbetween the monitoring engine 222 and the smart contract 226 stored onthe blockchain ledger 230. For example, the monitoring engine 222 mayinstall the smart contract 226 on a blockchain peer (not shown) thatmanages the blockchain ledger 230. There may be multiple peers thatco-manages the ledger and agree on changes to the blockchain ledger 230before they are made. Here, a communication channel may be establishedbetween the smart contract 226 on the blockchain peer/blockchain ledger230 and communication may be performed between the smart contract 226and the monitoring engine 222 via the communication channel.

The smart contract 226 may include read access to the blockchain 232 andmay transmit blockchain ledger content read from the blockchain 232and/or the blockchain ledger 230 to the monitoring engine 222 via theestablished communication channel. For example, code updates to theblockchain 232, blockchain transactions committed to the blockchain 232,updates to the users of the blockchain 232, and the like, may beforwarded to the monitoring engine 222 when detected/read by the smartcontract 226. The monitoring engine 222 may compare the received/readblockchain content from the smart contract 226 to stop conditions storedin the conditions database 224. If a stop condition isdetected/satisfied by a content update to the blockchain 232, themonitoring engine 222 may trigger a return of the investment in thedigital asset from the blockchain 232. Here, the host platform 220 maytrigger a crypto-exchange server to sell the digital asset, and ifnecessary, convert the proceeds from digital asset to fiat currency, andreturn the fiat currency to a user's financial account at a financialinstitution.

FIG. 2C illustrates a process 270 of monitoring the external service 250for start conditions (or stop conditions) in accordance with exampleembodiments. Referring to FIG. 2C, the user may integrate accesscredentials (username, password, PIN, account number, etc.) into themonitoring engine 222 via an application programming interface (API) 228of the monitoring engine 222. Here, the access credentials may be usedby the monitoring service 222 to establish a communication channel(authenticated) between the monitoring engine 222 and the externalservice 250. Accordingly, the monitoring engine 222 can monitor businesstransactions, sales, or the like, of the user within the externalservice 250 via the established channel, and compare the transactiondata to conditions in the conditions database 224 to determine whetherto start an investment or stop/return an investment. In this scenario,the monitoring engine 222 acts as a proxy for the user and bridgestogether the external service 250 and the temporary investment performedon a blockchain ledger.

FIG. 2D illustrates a process 280 of the host platform configuring andmanaging monitoring jobs using a job queue 290. In this example, thehost platform hosts the recommendation engine 122 and the monitoringengine 222, but embodiments are not limited thereto. In some cases, themonitoring engine 222 may be hosted separately from the recommendationengine 122 or not in communication with the other at all. It should alsobe appreciated that other systems and devices may be integrated.

Referring to FIG. 2D, the recommendation engine 122 may configure themonitoring engine 222 to perform monitoring jobs based on recommendedinvestments that are approved by a user. For example, the recommendationengine 122 may provide the monitoring engine 222 with details of eachmonitoring job including a smart contract identifier, a blockchain URL,a monitoring frequency, one or more monitoring rules or conditions, andthe like. The monitoring engine 222 may generate entries such as entries291 and 292 within the job queue 290 which are periodically executed bythe host platform. For example, a time-to-live (TTL) job or the like,may be programmed and executed for each job in the job queue 290. TheTTL may include a timer. When the timer expires, a notification may beforward from the TTL job or the host platform itself to the monitoringengine 222 to perform another analysis of the external data source.

FIG. 3A illustrates a communication sequence 300A of a temporaryinvestment process in accordance with example embodiments. Referring toFIG. 3A, a user may register with the host platform (e.g., arecommendation engine 310) in 331 via a user device 305 such as a mobiledevice which has a front-end of the software application installedtherein. The registration process may include the user installing afront-end of a software application hosted by the host platform anduploading account details such as accounts where the host platform isauthorized to analyze and make temporary investments to a back-end ofthe software application which includes the recommendation engine 310.The registration process may also enable the user to choose variousinvestment settings including preferred types of investments, stopconditions, investments not approved, and the like. In 332, therecommendation engine 310 may access a financial account of the user ata financial institution server 325 based on the account details providedduring the registration process. The recommendation engine 310 mayretrieve account history details in 333 from the financial account ofthe user.

In 334, the recommendation engine may input transaction history datainto one or more machine learning models which identify an availableamount of idle cash within the user's account which is safe forinvestment, and a temporary amount of time for the available amount ofidle cash to be invested. In 335, the recommendation engine 310 maytransmit a recommendation or recommendations to the user device 305 withoptions to allow the user to authorize such investments. As an example,FIG. 4 illustrates a process 400 of displaying a plurality of investmentoptions on a user interface of a front-end 424 of a software applicationinstalled on a user device 410. The options may be generated anddisplayed by a back-end 422 of the software application which is hostedby a host platform 400. Here, the host platform may correspond to thehost platform which hosts the recommendation engine 310 and themonitoring engine 315 shown in FIGS. 3A-3B.

Referring again to FIG. 3A, the user may approve of a recommendedinvestment option by pressing on a selection or inputting some sort ofcommand into the front-end of the software application in 336. Inresponse, the recommendation engine 310 may generate a pre-programmedblockchain wallet and install the wallet on a blockchain ledger 330 of aselected blockchain network where the investment is to take place in337. Furthermore, in 338, the recommendation engine 310 may trigger astart of the investment by sending a request to a crypto-exchange server320 with the details of the pre-programmed blockchain wallet such as awallet ID, URI, etc., and sending a request to the financial institutionserver 325 with an amount of money to forward to the crypto-exchangeserver 320.

In 339, the financial institution server 325 transfers fiat currency tothe crypto-exchange server 320, and in 341, the crypto-exchange server320 converts the fiat currency into a corresponding amount ofcryptocurrency or some other digital asset, and provides the digitalasset to the pre-programmed blockchain wallet stored on the blockchainledger 330. Furthermore, in 342, the crypto-exchange server 320 sendsconfirmation of the transfer to the recommendation engine 310.

FIG. 3B illustrates a communication sequence 300B of a securitymonitoring process in accordance with example embodiments. Here, thecommunication sequence 300B may be performed in sequence with thecommunication sequence 300A shown in FIG. 3A, however, embodiments arenot limited thereto, and these processes may be performed separately andmutually exclusively.

Referring to FIG. 3B, in 343, the recommendation engine 310 configuressettings for a monitoring job for monitoring the temporary investmentapproved by the user. For example, the configuring may configure themonitoring engine 315 with URLs, smart contract identifiers, frequenciesof monitoring, financial accounts to monitor and access credentials tosuch financial accounts, external services to monitor and accesscredentials to such external services, and the like. In 344, themonitoring engine 315 begins monitoring a financial account of the userto detect if the user's account balance falls below a predefinedthreshold. Likewise, in 345, the monitoring engine 315 installs a smartcontract on the blockchain ledger 330 and begins monitoring the ledgerfor security issues such as described in the processes of FIGS. 2A-2C.

In 346, as a result of the monitoring, the monitoring engine 310 detectsa stop condition which terminates an investment early. In 347, themonitoring engine 310 triggers a return of the investment with thecrypto-exchange server 320, for example, via a request message or APIcall, and in 348 the monitoring engine 310 triggers a return of theinvestment with the pre-programmed digital wallet installed on theblockchain ledger 330 which is pre-configured/hardcoded to return thefunds to the user's financial account to the crypto-exchange server 320.In response, the blockchain wallet returns the crypto-asset to thecrypto exchange server 320, which converts the crypto-asset to fiatcurrency in 350 and returns the fiat currency back to the user financialaccount at the financial institution server 325, in 351. In 352, thecrypto-exchange server 320 transmits confirmation of the return to therecommendation engine 310 which sends the confirmation to the userdevice 305.

FIG. 5 illustrates a method 500 for recommending and performing atemporary investment in accordance with example embodiments. Forexample, the method 500 may be performed by the host platform 120 shownin FIGS. 1A-1D, or the like. Referring to FIG. 5 , in 510, the methodmay include ingesting data records of a user from an external datasource via a backend of a software application hosted by a hostplatform. The data records may include account statements, transactionhistory, documents, and the like, with financial transaction history ofthe user. In 520, the method may include determining, via execution of amachine learning model on the ingested data records, a temporary valuethat is idle in an account of the user hosted by the external datasource and a period of time that the temporary value is idle based onthe ingested data records.

In 530, the method may include displaying, via a user interface on afront-end of the software application on a user device, the determinedtemporary value, the period of time, and one or more recommendations ofone or more blockchain networks. An example of the user interface isshown in FIG. 4 . In 540, the method may include receiving authorizationand a selection of a blockchain network from the user interface on thefront-end of the software application. In 550, the method may includeinstalling a pre-programmed blockchain wallet on a blockchain ledger ofthe selected blockchain network. In 560, the method may includetriggering a transfer of funds from the account of the user hosted bythe external data source to the pre-programmed blockchain wallet on theblockchain ledger via a crypto-exchange server which converts the fundsto cryptocurrency prior to the transfer.

In some embodiments, the installing may include pre-programming theblockchain wallet to stake cryptocurrency received from thecrypto-exchange server to a predefined smart contract on the blockchainledger of the selected blockchain network based on an identifier of thepredefined smart contract stored in the pre-programmed blockchainwallet. In some embodiments, the method may further include receivingaccess credentials of the user to a remote web service via anapplication programming interface (API) and establishing a remotemonitoring channel for monitoring user transactions performed via theremote web service based on the received access credentials. In someembodiments, the method may further include detecting a start conditionbased on the monitoring of the user transactions via the remotemonitoring channel, and in response, displaying, via the user interfaceon the front-end of the software application on the user device, thedetermined temporary value, the period of time, and the one or morerecommendations of one or more blockchain networks.

In some embodiments, the triggering may include transmitting a firsttrigger request message to the external data source which controls theexternal data source to submit funds to the crypto-exchange server, andtransmitting a second trigger request message to the crypto-exchangeserver which comprises a request to convert the funds to cryptocurrencyand a wallet identifier of the pre-programmed blockchain wallet on theblockchain ledger where the cryptocurrency should be delivered. In someembodiments, the method may further include receiving inputs via theuser interface of the front-end of the software application andconfiguring start and stop conditions of the transfer of funds within aconditions database using the received inputs from the user interface ofthe front-end of the software application.

In some embodiments, the method may further include detecting a stopcondition has occurred based on the stop conditions stored within theconditions database, and triggering the pre-programmed blockchain walletto return the cryptocurrency from the blockchain ledger to thecrypto-exchange server in response to the detected stop condition. Insome embodiments, the determining may include determining, via executionof the machine learning model, a maximum value in the account of theuser available for transfer to the pre-programmed blockchain walletbased on a pattern of recurring expenses in the account of the user, anddetermining the temporary value by subtracting a safety buffer valuefrom the maximum value. In some embodiments, the determining may includedetermining, via execution of the machine learning model, a temporaryperiod of time for investing the temporary value based on a pattern ofrecurring expenses in the account of the user.

FIG. 6 illustrates a method 600 for monitoring a temporary investment inaccordance with example embodiments. For example, the method 600 may beperformed by the host platform 120 shown in FIGS. 1A-1D, or the like.Referring to FIG. 6 , in 610, the method may include transmitting arequest to a crypto-exchange server to convert funds from an account ofa user with an external data source to a crypto asset and store thecrypto asset on a blockchain ledger of a blockchain network. Forexample, the funds may include cash or other assets stored within a bankaccount, credit account, checking account, savings account, payrollaccount, or the like.

In 620, the method may include installing a blockchain smart contract onthe blockchain ledger of the blockchain network with read access tocontent stored on the blockchain ledger. In 630, the method may includeestablishing a communication channel between a monitoring engine and theblockchain smart contract. In 640, the method may include configuringthe monitoring engine to identify stop conditions within content storedon the blockchain ledger. In 650, the method may include monitoring theblockchain ledger for updates to content stored on the blockchain ledgervia the communication channel between the monitoring engine and theblockchain smart contract and detecting a stop condition based on anupdate to the blockchain ledger via the monitoring engine. In 660, themethod may include, in response the detected stop condition,transmitting a request to the crypto-exchange server to return the fundsto the external data source.

In some embodiments, the monitoring may include iteratively readingupdates to content stored within the blockchain ledger via theblockchain smart contract and comparing the updates to predefinedsecurity risks that are stored in a security profile of the monitoringengine. In some embodiments, the monitoring may include executing a newiteration of the reading of the updates via the blockchain smartcontract each time a new block is added to the blockchain ledger by theblockchain network. In some embodiments, the configuring may furtherinclude establishing a communication channel between the monitoringengine and the external data source, configuring the monitoring engineto identify stop conditions based on changes to account data stored bythe external data source, and monitoring updates to the account data viathe established channel between the monitoring engine and the externaldata source.

In some embodiments, the monitoring may include monitoring for updatesto blockchain chaincode installed on the blockchain ledger that do notsatisfy a predetermined security criteria. In some embodiments, themonitoring may include monitoring for updates to user accounts of theblockchain ledger that are previously labeled as malicious useraccounts. In some embodiments, the monitoring may include monitoring forde-pegging of the digital asset via the blockchain ledger. In someembodiments, the configuring may include configuring the monitoringengine to trigger a return of the funds from the crypto-exchange serverat a default future time, and determining to request return of the fundsfrom the crypto-exchange server prior to the default future time inresponse to the detected stop condition.

FIG. 7 illustrates an example of a server node 700 according to someembodiments which may perform the role of the host platform 120 shown inFIGS. 1A-1D. The server node 700 may include a general-purpose computingapparatus and may execute program code to perform any of the functionsdescribed herein. The server node 700 may comprise an implementation ofa remote terminal or a host platform, in some embodiments. It shouldalso be appreciated that the server node 700 may include other unshownelements according to some embodiments and may not include all of theelements shown in FIG. 7 .

Server node 700 includes processing unit(s) 710 (i.e., processors)operatively coupled to communication device 720, data storage device730, input device(s) 740, output device(s) 750, and memory 760.Communication device 720 may facilitate communication with externaldevices, such as an external network or a data storage device. Inputdevice(s) 740 may comprise, for example, a keyboard, a keypad, a mouseor other pointing device, a microphone, knob or a switch, an infra-red(IR) port, a docking station, and/or a touch screen. Input device(s) 740may be used, for example, to enter information into the server node 700.Output device(s) 750 may comprise, for example, a display (e.g., adisplay screen) a speaker, and/or a printer.

Data storage device 730 may comprise any appropriate persistent storagedevice, including combinations of magnetic storage devices (e.g.,magnetic tape, hard disk drives and flash memory), optical storagedevices, Read Only Memory (ROM) devices, etc., while memory 760 maycomprise Random Access Memory (RAM). In some embodiments, the datastorage device 730 may store user interface elements in tabular form.For example, one or more columns and one or more rows of user interfaceelements may be displayed in a two-dimensional spreadsheet, table,document, digital structure, or the like.

Application server 731 and query processor 732 may each comprise programcode executed by processing unit(s) 710 to cause server node 700 toperform any one or more of the processes described herein. Suchprocesses may include estimating a selectivity of a query on tables 734based on statistics 733. Embodiments are not limited to execution ofthese processes by a single computing device. Data storage device 730may also store data and other program code for providing additionalfunctionality and/or which are necessary for operation of server node700, such as device drivers, operating system files, etc.

As will be appreciated based on the foregoing specification, theabove-described examples of the disclosure may be implemented usingcomputer programming or engineering techniques including computersoftware, firmware, hardware or any combination or subset thereof. Anysuch resulting program, having computer-readable code, may be embodiedor provided within one or more non transitory computer-readable media,thereby making a computer program product, i.e., an article ofmanufacture, according to the discussed examples of the disclosure. Forexample, the non-transitory computer-readable media may be, but is notlimited to, a fixed drive, diskette, optical disk, magnetic tape, flashmemory, external drive, semiconductor memory such as read-only memory(ROM), random-access memory (RAM), and/or any other non-transitorytransmitting and/or receiving medium such as the Internet, cloudstorage, the Internet of Things (IoT), or other communication network orlink. The article of manufacture containing the computer code may bemade and/or used by executing the code directly from one medium, bycopying the code from one medium to another medium, or by transmittingthe code over a network.

The computer programs (also referred to as programs, software, softwareapplications, “apps”, or code) may include machine instructions for aprogrammable processor, and may be implemented in a high-levelprocedural and/or object-oriented programming language, and/or inassembly/machine language. As used herein, the terms “machine-readablemedium” and “computer-readable medium” refer to any computer programproduct, apparatus, cloud storage, internet of things, and/or device(e.g., magnetic discs, optical disks, memory, programmable logic devices(PLDs)) used to provide machine instructions and/or data to aprogrammable processor, including a machine-readable medium thatreceives machine instructions as a machine-readable signal. The“machine-readable medium” and “computer-readable medium,” however, donot include transitory signals. The term “machine-readable signal”refers to any signal that may be used to provide machine instructionsand/or any other kind of data to a programmable processor.

The above descriptions and illustrations of processes herein should notbe considered to imply a fixed order for performing the process steps.Rather, the process steps may be performed in any order that ispracticable, including simultaneous performance of at least some steps.Although the disclosure has been described in connection with specificexamples, it should be understood that various changes, substitutions,and alterations apparent to those skilled in the art can be made to thedisclosed embodiments without departing from the spirit and scope of thedisclosure as set forth in the appended claims.

What is claimed is:
 1. A computing system comprising: a memoryconfigured to store a machine learning model; a network interfaceconfigured to ingest data records of a user from an external data sourcevia a backend of a software application hosted by a host platform; and aprocessor configured to determine, via execution of the machine learningmodel on the ingested data records, a temporary value that is idle in anaccount of the user hosted by the external data source and a period oftime that the temporary value is idle based on the ingested datarecords; display, via a user interface on a front-end of the softwareapplication on a user device, the determined temporary value, the periodof time, and one or more recommendations of one or more blockchainnetworks; receive authorization and a selection of a blockchain networkfrom the user interface on the front-end of the software application;install a pre-programmed blockchain wallet on a blockchain ledger of theselected blockchain network; and trigger a transfer of funds from theaccount of the user hosted by the external data source to thepre-programmed blockchain wallet on the blockchain ledger via acrypto-exchange server which converts the funds to cryptocurrency priorto the transfer.
 2. The computing system of claim 1, wherein theprocessor is further configured to pre-programming the blockchain walletto stake cryptocurrency received from the crypto-exchange server to apredefined smart contract on the blockchain ledger of the selectedblockchain network based on an identifier of the predefined smartcontract stored in the pre-programmed blockchain wallet.
 3. Thecomputing system of claim 1, wherein the processor is configured toreceive access credentials of the user to a remote web service via anapplication programming interface (API) and establish a remotemonitoring channel for monitoring user transactions performed via theremote web service based on the received access credentials.
 4. Thecomputing system of claim 3, wherein the processor is configured todetect a start condition based on the monitoring of the usertransactions via the remote monitoring channel, and in response,display, via the user interface on the front-end of the softwareapplication on the user device, the determined temporary value, theperiod of time, and the one or more recommendations of one or moreblockchain networks.
 5. The computing system of claim 1, wherein theprocessor controls the network interface to transmit a first triggerrequest message to the external data source which controls the externaldata source to submit funds to the crypto-exchange server, and transmita second trigger request message to the crypto-exchange server whichcomprises a request to convert the funds to cryptocurrency and a walletidentifier of the pre-programmed blockchain wallet on the blockchainledger where the cryptocurrency should be delivered.
 6. The computingsystem of claim 1, wherein the processor is further configured toreceive inputs via the user interface of the front-end of the softwareapplication and configure start and stop conditions of the transfer offunds within a conditions database using the received inputs from theuser interface of the front-end of the software application.
 7. Thecomputing system of claim 5, wherein the processor is further configuredto detect a stop condition has occurred based on the stop conditionsstored within the conditions database, and trigger the pre-programmedblockchain wallet to return the cryptocurrency from the blockchainledger to the crypto-exchange server in response to the detected stopcondition.
 8. The computing system of claim 1, wherein the processor isconfigured to determine, via execution of the machine learning model, amaximum value in the account of the user available for transfer to thepre-programmed blockchain wallet based on a pattern of recurringexpenses in the account of the user, and determine the temporary valueby subtracting a safety buffer value from the maximum value.
 9. Thecomputing system of claim 1, wherein the processor is configured todetermine, via execution of the machine learning model, a temporaryperiod of time for investing the temporary value based on a pattern ofrecurring expenses in the account of the user.
 10. A method comprising:ingesting data records of a user from an external data source via abackend of a software application hosted by a host platform;determining, via execution of a machine learning model on the ingesteddata records, a temporary value that is idle in an account of the userhosted by the external data source and a period of time that thetemporary value is idle based on the ingested data records; displaying,via a user interface on a front-end of the software application on auser device, the determined temporary value, the period of time, and oneor more recommendations of one or more blockchain networks; receivingauthorization and a selection of a blockchain network from the userinterface on the front-end of the software application; installing apre-programmed blockchain wallet on a blockchain ledger of the selectedblockchain network; and triggering a transfer of funds from the accountof the user hosted by the external data source to the pre-programmedblockchain wallet on the blockchain ledger via a crypto-exchange serverwhich converts the funds to cryptocurrency prior to the transfer. 11.The method of claim 10, wherein the installing further comprisespre-programming the blockchain wallet to stake cryptocurrency receivedfrom the crypto-exchange server to a predefined smart contract on theblockchain ledger of the selected blockchain network based on anidentifier of the predefined smart contract stored in the pre-programmedblockchain wallet.
 12. The method of claim 10, wherein the methodfurther comprises receiving access credentials of the user to a remoteweb service via an application programming interface (API) andestablishing a remote monitoring channel for monitoring usertransactions performed via the remote web service based on the receivedaccess credentials.
 13. The method of claim 12, wherein the methodfurther comprises detecting a start condition based on the monitoring ofthe user transactions via the remote monitoring channel, and inresponse, displaying, via the user interface on the front-end of thesoftware application on the user device, the determined temporary value,the period of time, and the one or more recommendations of one or moreblockchain networks.
 14. The method of claim 10, wherein the triggeringcomprises transmitting a first trigger request message to the externaldata source which controls the external data source to submit funds tothe crypto-exchange server, and transmitting a second trigger requestmessage to the crypto-exchange server which comprises a request toconvert the funds to cryptocurrency and a wallet identifier of thepre-programmed blockchain wallet on the blockchain ledger where thecryptocurrency should be delivered.
 15. The method of claim 10, whereinthe method further comprises receiving inputs via the user interface ofthe front-end of the software application and configuring start and stopconditions of the transfer of funds within a conditions database usingthe received inputs from the user interface of the front-end of thesoftware application.
 16. The method of claim 14, wherein the methodfurther comprises detecting a stop condition has occurred based on thestop conditions stored within the conditions database, and triggeringthe pre-programmed blockchain wallet to return the cryptocurrency fromthe blockchain ledger to the crypto-exchange server in response to thedetected stop condition.
 17. The method of claim 10, wherein thedetermining comprises determining, via execution of the machine learningmodel, a maximum value in the account of the user available for transferto the pre-programmed blockchain wallet based on a pattern of recurringexpenses in the account of the user, and determining the temporary valueby subtracting a safety buffer value from the maximum value.
 18. Themethod of claim 10, wherein the determining comprises determining, viaexecution of the machine learning model, a temporary period of time forinvesting the temporary value based on a pattern of recurring expensesin the account of the user.
 19. A non-transitory computer-readablemedium comprising instructions which when executed by a processor causea computer to perform a method comprising: ingesting data records of auser from an external data source via a backend of a softwareapplication hosted by a host platform; determining, via execution of amachine learning model on the ingested data records, a temporary valuethat is idle in an account of the user hosted by the external datasource and a period of time that the temporary value is idle based onthe ingested data records; displaying, via a user interface on afront-end of the software application on a user device, the determinedtemporary value, the period of time, and one or more recommendations ofone or more blockchain networks; receiving authorization and a selectionof a blockchain network from the user interface on the front-end of thesoftware application; installing a pre-programmed blockchain wallet on ablockchain ledger of the selected blockchain network; and triggering atransfer of funds from the account of the user hosted by the externaldata source to the pre-programmed blockchain wallet on the blockchainledger via a crypto-exchange server which converts the funds tocryptocurrency prior to the transfer.
 20. The non-transitorycomputer-readable medium of claim 19, wherein the installing furthercomprises pre-programming the blockchain wallet to stake cryptocurrencyreceived from the crypto-exchange server to a predefined smart contracton the blockchain ledger of the selected blockchain network based on anidentifier of the predefined smart contract stored in the pre-programmedblockchain wallet.